Microwave-Assisted Extraction of Phenolic Compounds from Broccoli (<i>Brassica oleracea</i>) Stems, Leaves, and Florets: Optimization, Characterization, and Comparison with Maceration Extraction
Bibliographic record
Abstract
Microwave-assisted extraction (MAE) to obtain phenolics from vegetable wastes has been of recent interest. Broccoli is one of the most globally produced vegetables, and around 43% of the harvest is considered waste. Thus, given the significant quantity of broccoli waste generated, the objective of this work was to optimize the MAE, to maximize the total phenolic content (TPC) from broccoli by-products (leaves and stems) and broccoli florets. The Response Surface Analysis was used in the optimization model to evaluate the impacts of methanol concentration, time, and temperature, and their interactions on the TPC of the broccoli extracts. The optimal MAE conditions were found to be 74.54% (methanol), 15.9 min, and 74.45 °C for broccoli stems; 80% (methanol), 10 min, and 73.27 °C for broccoli leaves; and 80% (methanol), 18.9 min, and 75 °C for broccoli florets. Under these conditions, the broccoli leaves exhibited the highest TPC (1940.35 ± 0.794 µg GAE/g DW), followed by the florets (657.062 ± 0.771 µg GAE/g DW) and stems (225.273 ± 0.897 µg GAE/g DW). The antioxidant activity of the broccoli extracts was evaluated under the optimal conditions by DPPH and ABTS assays, and the same behavior was observed in both studies, the broccoli leaves exhibited the highest antioxidant activity, among florets and stems. In addition, vanillic, sinapic, caffeic, chlorogenic, ferulic, gallic, neochlorogenic, and p-coumaric acids in the broccoli extracts were identified and quantified using HPLC. Furthermore, MAE was found to increase the phenolic yield up to 45.70% for broccoli leaves, 133.57% for broccoli florets, and 65.30% for broccoli stems, in less time compared with maceration extraction. MAE proved to be an efficient and sustainable technique to obtain phenolics from broccoli by-products, which can constitute a viable solution for valorizing broccoli wastes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".